Top Trends in Predictive Maintenance Technology Market 2022

Predictive maintenance is a novel technology in the manufacturing industry, and it aims to minimize the costly downtime due to untimely equipment failure and lack of machine maintenance. It helps businesses to continue their operation and enhance their productivity by accurately analyzing the present condition of their machinery and predicting the need to maintain and repair the machines. Sensors are employed in machines that capture real-time data and lower the risk of unplanned downtime during the manufacturing process.

The major trends in predictive maintenance technology that can transform the manufacturing industry in the coming years:

  1. Digital Twin Technology

Digital twin involves virtual simulation of physical manufacturing operations to monitor the working of machines involved in the process. Manufacturing industry market players can gather information about the whole system and find faults by predicting the maintenance requirements of the machines employed in the process. Digital Twin can also prove beneficial to the oil and gas industry to monitor oilfield equipment and accurately access the equipment’s present condition and provide feedback to help manufacturers make informed decisions.

  • Predictive Maintenance As A Service

Predictive Maintenance As A Service is a service, especially for original equipment manufacturers. It is expected to change the way OEMs collect real-time performance data from the equipment. OEMs can analyze, process, and generate individual insights and equipment-specific maintenance targets, which is expected to create lucrative opportunities for the global predictive maintenance market players in the coming years.

  • Artificial Intelligence Technology

Due to the highly competitive manufacturing industry, market players are willing to adopt advanced technologies and equipment to capture the highest market share and stay ahead in the market. Artificial intelligence interprets data by using consistently improving algorithms in less duration. Manufacturing industry players can use this data to help them scale their existing operations and processes at a much faster pace. Therefore, the Predictive Maintenance technology using artificial technology will improve the productivity and efficiency of the manufacturing industries.

  • Thermography Techniques

Thermography techniques is a non-destructive testing technique that makes the use of infrared scanners to identify the wear and rust of equipment. Heat is an early indicator of equipment damage or machine malfunction. Manufacturers prefer to have regular infrared predictive maintenance tests to save money on equipment repair and reactive maintenance fees. This technology is anticipated to be a promising technology as it can help avoid unplanned downtime and find potential failures that can lower the pace of the manufacturing process.

  • Smart Factory

The smart factory is a facility that uses a novel and advanced technologies that helps the manufacturing industries to make smart decisions based on data and insights. Predictive Maintenance technology is going to be a key technology in smart factories as they help generate insights and predict the maintenance of machines. A combination of a number of technologies, including machine learning, artificial intelligence, and connected sensors, along with a user-friendly human interface that can be operated directly or remotely, is expected to support the demand for smart factories worldwide.

  • Plug And Play Technology

Plug And Play technology allow manufacturers to connect with legacy machines without expensive machinery replacements. The majority of manufacturers depend on legacy equipment to run important application processes. Plug and Play devices do not require to undergo a complex installation process and are designed to work straight out of the box. They are directly used on machines and help generate real-time insights to improve working and detect failures in advance.    According to TechSci Research report on Predictive Maintenance Market— Global Industry Size, Share, Trends, Competition, Opportunity, and Forecast, 2016-2026, Segmented By Component (Service, Solution), By Testing Type (Vibration Analysis, Power System Assessment, Infrared Thermal Inspections, Insulating Fluid Analysis, Circuit Monitoring Analysis, Others), By Deployment (On-Premise, Cloud), By Organization Size (SME, Large Enterprise), By End User (Aerospace and Defense, Energy and Infrastructure, Logistics & Transportation, Manufacturing, Oil and Gas, Automotive, Retail and Ecommerce, Others), By Region” the global predictive maintenance market is expected to grow at a CAGR of 31.85% in the forecast period, 2022-2026, to reach USD22429.73 million by 2026. The key drivers for the global predictive maintenance market are growing investments by the market players to improve equipment quality and product quality and lower downtime during the manufacturing process.

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